from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 14 predictions (58.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_brute_force	predict	0.008	100000	1000	100	2.017979	0.154473	NaN	0.000396	0.002018	brute	-1	1	0.660	0.199977	0.007453	0.668	10.091032	10.098038
4	KNeighborsClassifier_brute_force	predict	0.084	100000	1000	100	3.014098	0.065176	NaN	0.000265	0.003014	brute	-1	5	0.752	0.200532	0.005155	0.668	15.030513	15.035478
5	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.026811	0.004060	NaN	0.000030	0.026811	brute	-1	5	1.000	0.009399	0.000400	0.000	2.852481	2.855065
7	KNeighborsClassifier_brute_force	predict	0.148	100000	1000	100	2.267967	0.015166	NaN	0.000353	0.002268	brute	1	100	0.877	0.206073	0.006429	0.729	11.005648	11.011002
10	KNeighborsClassifier_brute_force	predict	0.003	100000	1000	100	3.038351	0.053010	NaN	0.000263	0.003038	brute	-1	100	0.877	0.246444	0.002035	0.880	12.328788	12.329209
13	KNeighborsClassifier_brute_force	predict	0.023	100000	1000	100	2.241323	0.008436	NaN	0.000357	0.002241	brute	1	5	0.752	0.200128	0.001761	0.729	11.199463	11.199896
14	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.020937	0.000993	NaN	0.000038	0.020937	brute	1	5	1.000	0.008335	0.000150	0.000	2.511973	2.512382
16	KNeighborsClassifier_brute_force	predict	0.220	100000	1000	100	1.203705	0.018070	NaN	0.000665	0.001204	brute	1	1	0.660	0.249037	0.005219	0.880	4.833443	4.834504
19	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	2	1.752683	0.038020	NaN	0.000009	0.001753	brute	-1	1	0.962	0.031049	0.000360	0.955	56.449069	56.452859
22	KNeighborsClassifier_brute_force	predict	0.020	100000	1000	2	3.010746	0.068700	NaN	0.000005	0.003011	brute	-1	5	0.975	0.031038	0.000526	0.955	97.000557	97.014509
25	KNeighborsClassifier_brute_force	predict	0.022	100000	1000	2	2.214709	0.013407	NaN	0.000007	0.002215	brute	1	100	0.981	0.033113	0.002303	0.959	66.882436	67.043949
28	KNeighborsClassifier_brute_force	predict	0.014	100000	1000	2	2.922577	0.039057	NaN	0.000005	0.002923	brute	-1	100	0.981	0.072614	0.001733	0.967	40.248338	40.259801
31	KNeighborsClassifier_brute_force	predict	0.016	100000	1000	2	2.199197	0.013055	NaN	0.000007	0.002199	brute	1	5	0.975	0.033104	0.001916	0.959	66.433658	66.544801
34	KNeighborsClassifier_brute_force	predict	0.005	100000	1000	2	1.075944	0.007226	NaN	0.000015	0.001076	brute	1	1	0.962	0.071885	0.000933	0.967	14.967536	14.968796

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.767	0.0	-1	1	0.050	0.005	0.236	0.237	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.002	5.831	0.0	-1	5	0.049	0.000	0.282	0.282	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.845	0.0	1	100	0.053	0.004	0.220	0.220	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.713	0.0	-1	100	0.049	0.000	0.243	0.243	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.662	0.0	1	5	0.049	0.000	0.245	0.245	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.812	0.0	1	1	0.049	0.000	0.237	0.237	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.307	0.0	-1	1	0.010	0.000	0.548	0.548	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.304	0.0	-1	5	0.010	0.000	0.540	0.540	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.304	0.0	1	100	0.010	0.000	0.541	0.542	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.306	0.0	-1	100	0.010	0.000	0.533	0.533	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.312	0.0	1	5	0.010	0.000	0.526	0.527	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.312	0.0	1	1	0.010	0.000	0.522	0.522	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.018	0.154	0.000	0.002	-1	1	0.200	0.007	10.091	10.098	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	0.000	0.025	-1	1	0.008	0.000	3.083	3.085	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.014	0.065	0.000	0.003	-1	5	0.201	0.005	15.031	15.035	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.004	0.000	0.027	-1	5	0.009	0.000	2.852	2.855	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.268	0.015	0.000	0.002	1	100	0.206	0.006	11.006	11.011	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	100	0.008	0.000	2.621	2.622	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.038	0.053	0.000	0.003	-1	100	0.246	0.002	12.329	12.329	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.002	0.000	0.026	-1	100	0.009	0.001	3.025	3.040	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.241	0.008	0.000	0.002	1	5	0.200	0.002	11.199	11.200	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.001	0.000	0.021	1	5	0.008	0.000	2.512	2.512	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.204	0.018	0.001	0.001	1	1	0.249	0.005	4.833	4.835	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	1	0.008	0.000	2.383	2.384	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.753	0.038	0.000	0.002	-1	1	0.031	0.000	56.449	56.453	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.001	0.000	0.005	-1	1	0.001	0.000	5.856	5.882	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.011	0.069	0.000	0.003	-1	5	0.031	0.001	97.001	97.015	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.002	0.000	0.008	-1	5	0.001	0.000	9.329	9.375	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.215	0.013	0.000	0.002	1	100	0.033	0.002	66.882	67.044	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.378	3.388	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.923	0.039	0.000	0.003	-1	100	0.073	0.002	40.248	40.260	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	0.000	0.007	-1	100	0.001	0.000	8.356	8.461	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.199	0.013	0.000	0.002	1	5	0.033	0.002	66.434	66.545	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	3.333	3.349	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.076	0.007	0.000	0.001	1	1	0.072	0.001	14.968	14.969	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.446	2.458	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 12 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.006	1000000	1000	10	3.869139	1.394266	NaN	0.000021	0.003869	kd_tree	-1	100	0.957	0.111179	0.004506	0.951	34.800856	34.829432
4	KNeighborsClassifier_kd_tree	predict	0.006	1000000	1000	10	6.709369	1.659388	NaN	0.000012	0.006709	kd_tree	1	100	0.957	0.107449	0.001188	0.951	62.442211	62.446028
7	KNeighborsClassifier_kd_tree	predict	0.023	1000000	1000	10	1.133462	0.560180	NaN	0.000071	0.001133	kd_tree	1	1	0.942	0.571196	0.012918	0.965	1.984367	1.984875
10	KNeighborsClassifier_kd_tree	predict	0.004	1000000	1000	10	1.119099	0.387968	NaN	0.000071	0.001119	kd_tree	-1	5	0.966	0.192409	0.003831	0.970	5.816262	5.817415
13	KNeighborsClassifier_kd_tree	predict	0.028	1000000	1000	10	0.590886	0.140137	NaN	0.000135	0.000591	kd_tree	-1	1	0.942	0.215336	0.038485	0.970	2.744022	2.787502
16	KNeighborsClassifier_kd_tree	predict	0.001	1000000	1000	10	1.976438	0.434300	NaN	0.000040	0.001976	kd_tree	1	5	0.966	0.563245	0.015451	0.965	3.509021	3.510341
19	KNeighborsClassifier_kd_tree	predict	0.019	1000	1000	2	0.053876	0.022103	NaN	0.000297	0.000054	kd_tree	-1	100	0.915	0.000459	0.000046	0.896	117.348049	117.928624
22	KNeighborsClassifier_kd_tree	predict	0.019	1000	1000	2	0.040786	0.001468	NaN	0.000392	0.000041	kd_tree	1	100	0.915	0.000449	0.000031	0.896	90.762355	90.984156
25	KNeighborsClassifier_kd_tree	predict	0.033	1000	1000	2	0.021310	0.000415	NaN	0.000751	0.000021	kd_tree	1	1	0.890	0.005430	0.000126	0.923	3.924320	3.925381
28	KNeighborsClassifier_kd_tree	predict	0.017	1000	1000	2	0.028182	0.001262	NaN	0.000568	0.000028	kd_tree	-1	5	0.912	0.001580	0.000592	0.929	17.832270	19.041219
31	KNeighborsClassifier_kd_tree	predict	0.039	1000	1000	2	0.025366	0.000825	NaN	0.000631	0.000025	kd_tree	-1	1	0.890	0.000759	0.000026	0.929	33.401443	33.420335
34	KNeighborsClassifier_kd_tree	predict	0.011	1000	1000	2	0.023746	0.000578	NaN	0.000674	0.000024	kd_tree	1	5	0.912	0.005544	0.000016	0.923	4.283521	4.283539

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.388	0.038	0.024	0.0	-1	100	0.849	0.103	3.991	4.021	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.305	0.111	0.019	0.0	1	100	0.801	0.016	5.372	5.373	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.331	0.097	0.018	0.0	1	1	0.791	0.016	5.476	5.477	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.337	0.032	0.018	0.0	-1	5	0.787	0.007	5.511	5.511	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.306	0.178	0.019	0.0	-1	1	0.782	0.018	5.505	5.506	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.362	0.188	0.018	0.0	1	5	0.804	0.010	5.427	5.427	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.014	0.0	-1	100	0.003	0.002	0.329	0.395	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.025	0.0	1	100	0.002	0.002	0.354	0.471	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	1	0.001	0.000	0.588	0.591	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	5	0.001	0.000	0.576	0.581	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	1	0.001	0.000	0.613	0.613	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	5	0.001	0.000	0.620	0.620	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.869	1.394	0.000	0.004	-1	100	0.111	0.005	34.801	34.829	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.008	0.001	0.000	0.008	-1	100	0.000	0.000	29.939	31.187	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	6.709	1.659	0.000	0.007	1	100	0.107	0.001	62.442	62.446	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	1	100	0.000	0.000	18.600	19.449	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.133	0.560	0.000	0.001	1	1	0.571	0.013	1.984	1.985	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	1	0.001	0.000	1.660	1.716	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.119	0.388	0.000	0.001	-1	5	0.192	0.004	5.816	5.817	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	5	0.000	0.000	9.426	9.821	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.591	0.140	0.000	0.001	-1	1	0.215	0.038	2.744	2.788	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	1	0.000	0.000	7.590	7.991	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.976	0.434	0.000	0.002	1	5	0.563	0.015	3.509	3.510	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.001	0.000	2.276	2.358	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.054	0.022	0.000	0.000	-1	100	0.000	0.000	117.348	117.929	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.004	0.002	0.000	0.004	-1	100	0.000	0.000	41.624	44.042	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.041	0.001	0.000	0.000	1	100	0.000	0.000	90.762	90.984	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.602	6.855	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.000	0.001	0.000	1	1	0.005	0.000	3.924	3.925	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.510	5.813	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.028	0.001	0.001	0.000	-1	5	0.002	0.001	17.832	19.041	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	5	0.000	0.000	26.056	27.273	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.001	0.001	0.000	-1	1	0.001	0.000	33.401	33.420	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	24.960	26.161	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.001	0.001	0.000	1	5	0.006	0.000	4.284	4.284	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.128	6.467	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.677	0.078	30	0.024	0.0	k-means++	0.484	0.026	1.397	1.399	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.570	0.006	30	0.028	0.0	random	0.448	0.038	1.272	1.277	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.431	0.210	30	0.124	0.0	k-means++	2.897	0.026	2.220	2.220	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.053	0.179	30	0.132	0.0	random	2.715	0.014	2.229	2.229	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	k-means++	0.0	0.0	10.160	14.258	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	9.621	14.404	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	random	0.0	0.0	11.738	13.595	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	11.724	12.458	See	See
7	KMeans_tall	predict	1000000	1000	100	0.003	0.001	30	0.318	0.000	k-means++	0.0	0.0	8.500	8.993	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	k-means++	0.0	0.0	12.761	13.314	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.395	0.000	random	0.0	0.0	6.934	7.332	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	12.802	13.337	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 3 predictions (37.5%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
4	KMeans_short	predict	0.001605	10000	1000	2	0.002069	0.000122	20	0.007732	0.000002	full	k-means++	20	300	1	1.000000e-16	-0.000027	0.000526	0.000055	-0.001632	3.930419	3.951442
7	KMeans_short	predict	0.065016	10000	1000	100	0.003615	0.000738	20	0.221285	0.000004	full	random	20	300	1	1.000000e-16	0.346517	0.001122	0.000077	0.281501	3.220826	3.228461
10	KMeans_short	predict	0.047545	10000	1000	100	0.002951	0.000217	20	0.271131	0.000003	full	k-means++	20	300	1	1.000000e-16	0.358075	0.001104	0.000091	0.310531	2.671704	2.680713

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.088	0.002	20	0.002	0.0	random	0.032	0.005	2.727	2.761	See	See
3	KMeans_short	fit	10000	10000	2	0.248	0.004	20	0.001	0.0	k-means++	0.098	0.002	2.546	2.547	See	See
6	KMeans_short	fit	10000	10000	100	0.225	0.003	20	0.036	0.0	random	0.125	0.002	1.802	1.803	See	See
9	KMeans_short	fit	10000	10000	100	0.663	0.009	20	0.012	0.0	k-means++	0.348	0.007	1.903	1.904	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.008	0.000	random	0.001	0.0	4.173	4.191	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	0.000	0.0	13.510	13.910	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.008	0.000	k-means++	0.001	0.0	3.930	3.951	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	11.983	12.450	See	See
7	KMeans_short	predict	10000	1000	100	0.004	0.001	20	0.221	0.000	random	0.001	0.0	3.221	3.228	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	0.000	0.0	10.709	10.997	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.271	0.000	k-means++	0.001	0.0	2.672	2.681	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	10.424	10.650	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 3 predictions (75.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	LogisticRegression	predict	0.023	1000000	1000	100	0.000435	0.000482	[19]	1.837257	4.354318e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.495	0.000861	0.001470	0.472	0.505797	1.001117
4	LogisticRegression	predict	0.020	1000	100	10000	0.001844	0.000183	[26]	4.337631	1.844325e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.210	0.003578	0.000183	0.230	0.515429	0.516104
5	LogisticRegression	predict	1.000	1000	1	10000	0.000078	0.000023	[26]	1.031785	7.753550e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.000	0.000762	0.000063	1.000	0.101719	0.102066

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.650	0.507	[19]	0.069	0.000	2.002	0.022	5.820	5.821	See	See
3	LogisticRegression	fit	1000	1000	10000	1.021	0.716	[26]	0.078	0.001	0.789	0.043	1.293	1.295	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[19]	1.837	0.0	0.001	0.001	0.506	1.001	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[19]	0.013	0.0	0.000	0.000	0.367	0.374	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.338	0.0	0.004	0.000	0.515	0.516	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.032	0.0	0.001	0.000	0.102	0.102	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 1 prediction (25.0%).
The observed differences can be found in the diff_r2_scores column.
The chosen difference threshold is 0.001 for r2_score.
See details in the dataframe below.

	estimator	function	diff_r2_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup
1	Ridge	predict	0.001646	1000	1000	10000	0.010251	0.000205	NaN	7.804134	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.107435	0.016325	0.000273	0.10908	0.627943	0.628031

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.197	0.005	0.406	0.0	0.200	0.002	0.988	0.988	See	See
3	Ridge	fit	1000000	1000000	100	1.129	0.048	0.709	0.0	0.243	0.007	4.653	4.655	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	7.804	0.0	0.016	0.0	0.628	0.628	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.178	0.0	0.000	0.0	0.598	0.648	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	5.147	0.0	0.000	0.0	0.496	0.617	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.015	0.0	0.000	0.0	0.606	0.642	See	See